Teaching
Fall 2026–2027Course materials, lecture slides (PDF), and live interactive Google Colab notebooks.
ECO447 Machine Learning for Economists
Undergraduate • Department of Economics, Hacettepe UniversityBridge between econometrics and ML: supervised models, regularization, trees, causality, and clustering.
| Week | Topic | Empirical Lab / Dataset | Materials |
|---|---|---|---|
| W1 | Introduction to Machine Learning | California Housing & Macro ETFs | Slides |
| W2 | Causality: Theory & Data | Causality & Empirical Identification | Slides |
| W3 | Data Analysis & Exploratory Data Analysis (EDA) | Exploratory Data Analysis Workflow | Slides |
| W4 | Introduction to Regression | Linear Regression Modeling | Slides |
| W5 | Logistic Regression & Classification | Classification Metrics & Logit | Slides |
| W6 | Regularization: Ridge & LASSO | Penalized Regression & CV | Slides |
| W7 | Decision Trees | Tree-Based Modeling & Pruning | Slides |
| W8 | Advanced DT: Classification & Regression | Advanced Tree Architectures | Slides |
| W9 | Model Trees | Model Trees on Economic Data | Slides |
| W10 | Ensemble Methods | Random Forests & OOB Estimation | Slides |
| W11 | Boosting: AdaBoost | AdaBoost Implementation & Tuning | Slides |
| W12 | Causal Trees | Heterogeneous Treatment Effects | Slides |
| W13 | Clustering | K-Means & Hierarchical Clustering | Slides |
| W14 | Similarity Measures & Term Review | Metric Space & Distances in Python | Slides |
SEN603 Data Analytics and Statistics
M.Sc. (Thesis) in Systems Engineering • Hacettepe University
Assessment: Weekly HW (26%), Midterm (25%), Research Presentations (15%), Final Exam (34%).
Reference Documents:
Final Project Guide
Midterm Paper
Midterm Solutions
| Week | Topic & Syllabus Mapping | Applied Focus | Materials |
|---|---|---|---|
| W1 | Statistics, Data Science and Python Setup | Colab & Pandas Fundamentals | Slides |
| W2 | Data Exploration & EDA | Distributions, Boxplots, Outliers | Slides |
| W3 | Inference Basics: Permutation & p-values | Randomization Tests in Python | Slides |
| W4 | Random Numbers & Simulation | Monte Carlo Simulation | Slides |
| W5 | Probability & the Normal Distribution | Theoretical Distributions & QQ-Plots | Slides |
| W6 | Categorical Data & Chi-Square Tests | Contingency Tables & Goodness-of-Fit | Slides |
| W7 | Midterm Review & Examination | Midterm Assessment | Review |
| W8 | Sampling & Bootstrap Confidence Intervals | Resampling & Empirical CIs | Slides |
| W9 | ANOVA Basics | One-way & Two-way ANOVA | Slides |
| W10 | Correlation & Association | Pearson, Spearman, and Collinearity | Slides |
| W11 | Simple Linear Regression | OLS Diagnostics with Statsmodels | Slides |
| W12 | Multiple Regression | Multivariate Model Diagnostics | Slides |
| W13-14 | Research Presentations | Student Term Presentations | Slides |
SEN605 Financial Systems and Data Analytics
Graduate School of Informatics • Hacettepe University
Workload & Assessment: Presentation (20%), Midterm (30%), Final Exam (50%).
Course Focus: Systems engineering principles applied to financial markets, volatility modeling (GARCH), recurrent architectures, network contagion, and financial sentiment analysis.
| Week | Topic & Theoretical Scope | Notebook / Lab Focus | Materials |
|---|---|---|---|
| W1 | Intro to Systems Engineering & Financial Systems | Setup: yfinance, pandas, stock data | Slides |
| W2 | Financial Data Structures & Python Ecosystem | BIST-100 & S&P 500 OHLCV pipeline | Slides |
| W3 | Financial Time Series: Stationarity & ARIMA | USD/TRY ARIMA modeling | Slides |
| W4 | Volatility in Financial Systems: ARCH & GARCH | GARCH on BIST-30 equities | Slides |
| W5 | ML in Financial Forecasting I: Linear & Logistic | Predicting BIST-100 market direction | Slides |
| W6 | ML in Financial Forecasting II: Trees & Ensembles | XGBoost return predictor with SHAP | Slides |
| W7 | Deep Learning: Foundations of ANNs | PyTorch MLP for volatility prediction | Slides |
| W8 | MIDTERM EXAM | Theory & Applied Examination | Exam Session |
| W9 | Dynamic System Models: RNN, LSTM & GRU | LSTM 5-day forecast for USD/TRY & BTC | Slides |
| W10 | Financial Text Mining & NLP | TF-IDF + LDA on BIST earnings releases | Slides |
| W11 | Financial Sentiment Analysis with LLMs (FinBERT) | FinBERT pipeline for news headlines | Slides |
| W12 | Network Theory & Financial Contagion Effect | BIST-100 MST network visualization | Slides |
| W13 | Financial Risk Analysis & Portfolio Optimization | Monte Carlo VaR & Efficient Frontier | Slides |
| W14 | General Review & Project Clinic | Feedback on term projects & code review | Slides |